1.2 Lasso regression 1.3 ElasticNet 二 算法实战 2.1 导入包 import numpy as npimport pandas as pdfrom sklearn import datasetsfrom sklearn.model_selection import train_test_split, GridSearchCVfrom sklearn.linear_model import Ridge, Lasso...
1.1 Ride regression 1.2 Lasso regression 1.3 ElasticNet 二 算法实战 2.1 导入包 importnumpyasnpimportpandasaspdfromsklearnimportdatasetsfromsklearn.model_selectionimporttrain_test_split,GridSearchCVfromsklearn.linear_modelimportRidge,Lasso,ElasticNetfromsklearn.metricsimportmean_squared_error,r2_score 2.2 ...
importnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.metricsimportr2_score#def main():# 产生一些稀疏数据np.random.seed(42)n_samples,n_features=50,100X=np.random.randn(n_samples,n_features)# randn(...)产生的是正态分布的数据coef=3*np.random.randn(n_features)# 每个特征对应一个系数inds=np....
import numpy as np from sklearn.linear_model import LogisticRegression,LogisticRegressionCV from sklearn import datasets from sklearn.model_selection import train_test_split X,y = datasets.load_iris(True) X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2) lr = Logisti...
套索回归(Lasso Regression) 弹性网回归(ElasticNet) <1.1>线性回归 1.1.1、Python实现线性回归 # 基于sklearn实现线性回归 ### # 线性回归:sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=None) # (1)fit_intercept:是否使用截距(默认True)。True表示使用...
答案:Ridge Regression和Lasso Regression,都是对模型加入正则化项,惩罚过 大的参数, 以避免过拟合问题。其中,Lasso Regression采取L1正则化,而Ridge RegressionL2 化。 Sklearn库中Ridge Regression 和Lasso Regression模型的使用,参见源码包中 “第3 课线性回归”目录下教学案例中的源代码文件“ CO3-3SKlearn-Adsan...
import sklearn from sklearn.linear_model import LinearRegression X= [[0, 0], [1, 2], [2, 4]] y= [0,1,2] clf= LinearRegression() #fit_intercept=True #默认值为True,表示计算随机变量,False表示不计算随机变量 #normalize=False
In addition to the data-fidelity term corresponding to a linear regression, we penalize the L1 norm of the image to account for its sparsity. The resulting optimization problem is called the Lasso. We use the class sklearn.linear_model.Lasso, that uses the coordinate descent algorithm. ...
importnumpyasnpfromsklearn.linear_modelimportLinearRegression,Ridge,Lasso# 将数据一分为二fromsklearn.model_selectionimporttrain_test_split# 均方误差fromsklearn.metricsimportmean_squared_errorimportpandasaspd# 加载数据# 加载训练数据# train = pd.read_table('./zhengqi_train.txt') 和下面一行的效果相同tr...
lasso_regression importnumpyasnp fromsklearn.linear_modelimportSGDRegressor np.random.seed(1) X=2*np.random.rand(100,1) y=4+3*X+np.random.randn(100,1) sgd_reg=SGDRegressor(penalty='l1',max_iter=10000) sgd_reg.fit(X,y.ravel())#这里可以用ravel(),将2维列向量修改为1维行向量(元组),...